{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dagstermill\n",
    "import pandas as pd\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "from dbt_example.resources import postgres\n",
    "from dagster import ModeDefinition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "run_results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-23 14:16:58 - dagster - DEBUG - ephemeral_dagstermill_pipeline - 5f61f9ad-b10d-4ff0-be51-b17d18d8d7b5 - 1051 - this_solid.compute - ENGINE_EVENT - Starting initialization of resources [db].\n",
      "2020-10-23 14:16:58 - dagster - DEBUG - ephemeral_dagstermill_pipeline - 5f61f9ad-b10d-4ff0-be51-b17d18d8d7b5 - 1051 - this_solid.compute - ENGINE_EVENT - Finished initialization of resources [db].\n"
     ]
    }
   ],
   "source": [
    "context = dagstermill.get_context(\n",
    "    mode_def=ModeDefinition(resource_defs={'db': postgres}),\n",
    "    run_config={\n",
    "        'resources': {\n",
    "            'db': {'config': {'db_url': 'postgresql://dbt_example:dbt_example@localhost:5432/dbt_example'}}\n",
    "        }\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with context.resources.db.connect() as conn:\n",
    "    normalized_cereals = pd.read_sql('select * from normalized_cereals', con=conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>mfr</th>\n",
       "      <th>type</th>\n",
       "      <th>normalized_calories</th>\n",
       "      <th>normalized_protein</th>\n",
       "      <th>normalized_fat</th>\n",
       "      <th>normalized_sodium</th>\n",
       "      <th>normalized_fiber</th>\n",
       "      <th>normalized_carbo</th>\n",
       "      <th>normalized_sugars</th>\n",
       "      <th>normalized_potass</th>\n",
       "      <th>normalized_vitamins</th>\n",
       "      <th>shelf</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100% Bran</td>\n",
       "      <td>N</td>\n",
       "      <td>C</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
       "      <td>68.402973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100% Natural Bran</td>\n",
       "      <td>Q</td>\n",
       "      <td>C</td>\n",
       "      <td>120.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>33.983679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All-Bran</td>\n",
       "      <td>K</td>\n",
       "      <td>C</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
       "      <td>59.425505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>All-Bran with Extra Fiber</td>\n",
       "      <td>K</td>\n",
       "      <td>C</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.50</td>\n",
       "      <td>93.704912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Almond Delight</td>\n",
       "      <td>R</td>\n",
       "      <td>C</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>34.384843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name mfr type  normalized_calories  \\\n",
       "0                  100% Bran   N    C                 70.0   \n",
       "1          100% Natural Bran   Q    C                120.0   \n",
       "2                   All-Bran   K    C                 70.0   \n",
       "3  All-Bran with Extra Fiber   K    C                 50.0   \n",
       "4             Almond Delight   R    C                110.0   \n",
       "\n",
       "   normalized_protein  normalized_fat  normalized_sodium  normalized_fiber  \\\n",
       "0                 4.0             1.0              130.0              10.0   \n",
       "1                 3.0             5.0               15.0               2.0   \n",
       "2                 4.0             1.0              260.0               9.0   \n",
       "3                 4.0             0.0              140.0              14.0   \n",
       "4                 2.0             2.0              200.0               1.0   \n",
       "\n",
       "   normalized_carbo  normalized_sugars  normalized_potass  \\\n",
       "0               5.0                6.0              280.0   \n",
       "1               8.0                8.0              135.0   \n",
       "2               7.0                5.0              320.0   \n",
       "3               8.0                0.0              330.0   \n",
       "4              14.0                8.0               -1.0   \n",
       "\n",
       "   normalized_vitamins  shelf  weight  cups     rating  \n",
       "0                 25.0      3       1  0.33  68.402973  \n",
       "1                  0.0      3       1  1.00  33.983679  \n",
       "2                 25.0      3       1  0.33  59.425505  \n",
       "3                 25.0      3       1  0.50  93.704912  \n",
       "4                 25.0      3       1  0.75  34.384843  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_cereals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormap = {'H':'orangered', 'C':'cornflowerblue'}\n",
    "\n",
    "xs = normalized_cereals['normalized_calories'].apply(lambda x: x + random.random() * 3 - 1.5)\n",
    "ys = normalized_cereals['normalized_protein'].apply(lambda x: x + random.random()/10 - .05)\n",
    "colors = normalized_cereals['type'].apply(lambda x: colormap[x])\n",
    "labels = normalized_cereals['name']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "ax.scatter(x=xs, y=ys, c=colors, alpha=0.5)\n",
    "ax.set_xlabel(\"calories\")\n",
    "ax.set_label(\"protein\")\n",
    "\n",
    "for x, y, label in zip(xs, ys, labels):\n",
    "        plt.annotate(\n",
    "            label,\n",
    "            (x,y),\n",
    "            textcoords=\"offset points\",\n",
    "            xytext=(3, 10),\n",
    "            ha='left',\n",
    "            size=7\n",
    "        )\n",
    "\n",
    "plt.show()\n",
    "plot_path = 'cereal_analysis_{run_id}.pdf'.format(run_id=context.run_id)\n",
    "fig.savefig(plot_path, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cereal_analysis_5f61f9ad-b10d-4ff0-be51-b17d18d8d7b5.pdf'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dagstermill.yield_result(plot_path)"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
